**1. Introduction**

Planning electricity systems in terms of generation, transmission, and distribution relies on generation and load forecasting. In addition to economic load dispatching, unit commitment, and price forecasting which, are interests of the electricity market, electrical load forecasting is important in terms of risk reduction for the power grid. In addition, unbalanced supply/demand caused by inaccurate forecasts in traditional electricity generation systems [1] has led to integration of advanced communication technologies into traditional grids, which are known as smart girds (Figure 1). Smart grids engage the customer in the decision-making process and, in a larger view, decisions are made based on the flow and exchange of information [2]. However, there are challenges to ensure that smart grids are economically beneficial, such as closing the gap between demand and supply, and fuel resource planning. All these factors highlight the importance of accurate electrical energy demand forecasts.

Diverse techniques have been applied in demand forecasting problems such as techniques based on time series and regression analysis [3–5]. However, because of the non-linear nature of the problem, techniques based on artificial neural networks and Adaptive Neuro-Fuzzy Inference System (ANFIS) are more popular [6–11]. As an example, Barak and Sadegh [12] proposed a hybrid ARIMA-ANFIS model for forecasting of the annual energy consumption of Iran. ARIMA outputs were used to forecast the energy consumption, using different ANFIS structures. According to the results, the ARIMA-ANFIS

model gave more accurate forecasts compared to the ARIMA and ANFIS models. As the final step, meta-heuristic algorithms were employed to increase the accuracy of the ANFIS. The research does not develop a strategy for large data sets and input selection.

**Figure 1.** Conceptual diagram of smart grid [2].

In another piece of research conducted by Hooshmand et al. [13], a wavelet transform (WT) and an artificial neural network (ANN) were used for primary load forecasting where the inputs are meteorological parameters and previous values of the electric load. The ANFIS was employed to improve the forecasting results. However, the research does not introduce an approach for input selection and the capability of the evolutionary algorithms for optimizing the ANFIS has not been investigated. In another similar model, Panapakinis and Dagoumas [14] proposed a wavelet transform-ANFIS-GA-neural network model for natural gas demand forecasting. The original signal was decomposed by WT and used as ANFIS inputs. After optimizing ANFIS parameters with GA, output of ANFIS was fed into the neural network. The model does not seem to be efficient in case of multiple inputs since feature selection approach has not been developed.

A difference seasonal auto-regressive integrated moving average (diff-SARIMA), neural network, ANFIS, and DE combined method was used by Yang et al. [15] for short-term electricity demand forecasting of New South Wales in Australia. The proposed combined model presented better results than SARIMA, neural network, and ANFIS models. Parameters of the ANFIS were optimized using the DE method. Identical to the articles mentioned earlier, the research does not present a strategy for input selection. Moon et al. [16] proposed a hybrid of random forest and multi-layer perceptron for daily energy demand forecasting of a university campus. A decision tree was employed to classify the data into date, day of the week, holiday, and academic year. Furthermore, an approach was developed for considering the effect of the temperature in energy consumption and classifying the days of the week. However, the algorithm might not easily adapt to availability of the other parameters to be considered.

All the issues mentioned earlier motivated the current study to develop a forecasting strategy which: (i) can perform with any given dataset; (ii) is totally automatized; and (iii) provides a better accuracy.
